Computer-aided arrhythmia diagnosis with bio-signal processing: A survey of trends and techniques

Sai Manoj Pudukotai Dinakarrao, Axel Jantsch, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review


Signals obtained from a patient, i.e., bio-signals, are utilized to analyze the health of patient. One such bio-signal of paramount importance is the electrocardiogram (ECG), which represents the functioning of the heart. Any abnormal behavior in the ECG signal is an indicative measure of a malfunctioning of the heart, termed an arrhythmia condition. Due to the involved complexities such as lack of human expertise and high probability to misdiagnose, long-term monitoring based on computer-aided diagnosis (CADiag) is preferred. There exist various CADiag techniques for arrhythmia diagnosis with their own benefits and limitations. In this work, we classify the arrhythmia detection approaches that make use of CADiag based on the utilized technique. A vast number of techniques useful for arrhythmia detection, their performances, the involved complexities, and comparison among different variants of same technique and across different techniques are discussed. The comparison of different techniques in terms of their performance for arrhythmia detection and its suitability for hardware implementation toward body-wearable devices is discussed in this work.

Original languageEnglish (US)
Article numbera23
JournalACM Computing Surveys
Issue number2
StatePublished - May 2019


  • Arrhythmia detection
  • Computer-aided diagnosis
  • Electrocardiogram (ECG)
  • Health-care
  • Machine learning
  • Neural networks
  • Support-vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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